code: def __init__(self):
      super(TextCNN, self).__init__()
  
      embed_dim = 300
      vocab_size = 50265
      num_filters = [128, 128, 128, 128, 128]
      filter_sizes = [1, 2, 3, 4, 5]
      dropout = 0.5
  
      self.feature_dim = sum(num_filters)
      self.embeder = EmbeddingLayer(vocab_size, embed_dim)
      self.convs = nn.ModuleList(
          [
              nn.Conv2d(1, n, (f, embed_dim))
              for (n, f) in zip(num_filters, filter_sizes)
          ]
      )
  
      self.dropout = nn.Dropout(dropout)
      self.fc = nn.Sequential(
          self.dropout,
          nn.Linear(self.feature_dim, int(self.feature_dim / 2)),
          nn.ReLU(),
          nn.Linear(int(self.feature_dim / 2), 2),
      )
  
file_path: TextBox/textbox/evaluator/style_evaluator.py
imports: from .abstract_evaluator import AbstractEvaluator
  import torch
  import torch.nn
  import torch.nn.functional
  from math import ceil
  from transformers import BartTokenizerFast
label_desc: Use image node_id 1 to create a new TextCNN object from inherited base classes: nn with example: obj = TextCNN()